Time-domain optical medical imaging shows great promise as a technique for imaging breast tissue, as well as the brain and other body parts. The principle of the technique lies in the analysis of the temporal point spread function (TPSF) of an injected pulse of light which is diffused in an object of interest (OOI), and in the extraction of information from the TPSF to construct a medically useful image. The (complete) TPSF is defined as the light intensity of a given wavelength presented as a function of time t (to≦t≦∞) that can be observed at a given spatial location after a very short pulse of light produced at another spatial location at the time to.
Specific types of data extracted from the TPSF are known as ‘Data-types’. For example, ‘early time-gated attenuation’ is a Data-type that can be extracted from the TPSF which improves the image spatial resolution over continuous wave methods. However, it is unclear whether such improvements in image spatial resolution are adequate for diagnosing certain diseases, such as breast cancer, based on morphology.
An alternative approach is to use the TPSF to decouple the light attenuation into absorption and scattering components. This extra information, which generally cannot be obtained with continuous wave methods, may be clinically useful. In order to achieve this it is customary to extract appropriate Data-types from the TPSF. Researchers, in the time-domain field, have looked at data-types such as the meantime and higher moments of the TPSF (Arridge, 1999). Whilst data-types for optical data acquired in the frequency domain have included phase-shift and amplitude.
There are several techniques for measuring the TPSF arising from a light pulse that has traveled through an OOI such as breast tissue. Certainly the most information one can acquire is by measuring a complete TPSF. This information can also be acquired in the frequency domain, although current hardware limitations mean that time-domain hardware is capable of acquiring information over a larger bandwidth than its frequency domain counterpart.
However, acquiring a practical approximation f(t), tε[a,b] to the complete TPSF, such that
                    ∫        a        b            ⁢                        f          ⁡                      (            t            )                          ⁢                                  ⁢                  ⅆ          t                      ≈                  ∫                  t          0                ∞            ⁢                        f          ⁡                      (            t            )                          ⁢                                  ⁢                  ⅆ          t                      ,has implications in terms of long acquisition times for clinical systems, and possibly unacceptably long data-processing for image reconstruction algorithms.
It would therefore be desirable to provide methods overcoming the limitations of the prior art.